⚖️ The Pareto Frontier — Efficiency vs Intelligence

Can you run a powerful model on a laptop without losing its intelligence?

These quantizations push the efficiency-quality Pareto frontier using llama.cpp's K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

📦 Available Files

💡 Which file should I download?

• Most users: Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf — best balance of size and quality
• High RAM (32GB+): Kimi-Linear-48B-A3B-Instruct-Q8_0.gguf — near-original quality
• Low RAM (8GB): Kimi-Linear-48B-A3B-Instruct-Q3_K_M.gguf — fits in 8GB with room to spare

⚡ Speed Benchmarks

Run python benchmark.py --model Kimi-Linear-48B-A3B-Instruct to generate speed results.

🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

🚀 How to Use

Ollama

ollama run dhptl/kimi-linear-48b-a3b-instruct

LM Studio / Jan / Open WebUI

Search for Dhptl/Kimi-Linear-48B-A3B-Instruct in the model browser.

llama.cpp CLI

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --conversation \
  -n 512

Python — llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="./Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf",
    n_gpu_layers=-1,   # -1 = offload everything to GPU
    n_ctx=4096,
)

response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Tell me about quantization."}
])
print(response["choices"][0]["message"]["content"])

🔍 About GGUF Quantization

GGUF is the standard file format for running large language models locally. Quantization reduces the number of bits per weight:

💬 Community & Feedback

Found an issue? Have a question? Open a Discussion in the Community tab above.

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• ⭐ Starring quant-kit on GitHub
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